Bodyweight Changes During COVID-19 for Patients Diagnosed with Depression: A Retrospective Cohort Study.

Context: The COVID-19 pandemic led to an unprecedented lockdown of millions of Americans from the spring of 2020 to the fall of 2020. Studies done on the impact of COVID-19 on mental health and body weight have been important to our understanding of the effects of the pandemic. However, these studies on depression and BMI change have not identified a possible direction of the causality of the relationship between depression and body weight as affected by lockdown measures during a pandemic. Objective: The purpose of this study was to examine whether a diagnosis of depression is associated with changes in BMI during the COVID-19 pandemic for adults (aged ≥18 years). Study Design and Analysis: Retrospective cohort study Setting or Dataset: EHR data from a family medicine university clinic. Population Studied: Adults ≥18 years who visited the clinic within a 6-month period prior to lockdown (October 2019-March 2020) and at least once in the 6-month post-lockdown period (September 2020-March 2021). The lockdown period started in March 2020. 1,211 patients were included. Outcome Measures: Dependent variable: change in BMI; Primary independent variable: diagnosis of depression; Confounding variables: age, race/ethnicity, sex, medications, and chronic conditions Results: Mean age was 59.9 (sd=16.5). Patients were mostly female (n=770, 63.6%), white (n=678, 56.0%), and non-Hispanic (n=622, 51.4%). 18.7% (n=227) had a diagnosis of depression. There was a significant difference in BMI change (p<0.001) between the group diagnosed with depression (mean change=2.11, sd=1.9) and the group with no depression diagnosis (mean change=1.67, sd=1.9). Similarly, a diagnosis of depression significantly predicted BMI changes (p >0.001]). This association remained while including confounding variables in the model (p=0.009). Further statistical analysis showed that age between 31 and 50 significantly predicted BMI changes in those patients with no depression diagnosis while controlling for confounding variables (p=0.027). Conclusion: Individuals with depression had significant changes in BMI during the COVID-19 pandemic, and age predicted these changes in middle-aged adults (30-50 years old). These findings highlight the importance of identifying and following up with individuals with a diagnosis of depression to alleviate effects on their BMI during extended isolation. Identifying patients who might be susceptible to these changes could lead to patient health outcomes.

Association of hypothyroidism with low-level arsenic exposure in rural West Texas.

It has been reported recently that a higher airborne arsenic level was correlated with higher urinary arsenic concentration and lower serum thyroxin level among urban policemen and rural highway workmen in Italy. The current study was to determine whether exposure to low-level arsenic groundwater (2-22µg/L) is associated with hypothyroidism among 723 participants (118 male and 267 female Hispanics; 108 male and 230 female non-Hispanic whites, NHW) living in rural West Texas counties. Arsenic and iodine levels in their groundwater used for drinking and or cooking were estimated by the inverse distance weighted (IDW) interpolation technique. Groundwater arsenic was ≥8µg/L in 36% of the subjects' wells while iodine concentration was <1µg/L in 91% of their wells. Logistic regression analysis showed that arsenic in groundwater ≥8µg/L and cumulative arsenic exposure (groundwater arsenic concentration multiplied by the number of years living in the current address) but not groundwater iodine concentration were significant predictors for hypothyroidism among Hispanics (p<0.05) but not NHW after adjusting for covariates such as age, gender, annual household income and health insurance coverage. The ethnic difference may be due to a marginally higher percentage of Hispanics (p=0.0622) who lived in areas with groundwater arsenic ≥8µg/L compared with NHW. The prevalence of hypothyroidism was significantly higher in Hispanics or NHW of this rural cohort than the national prevalence. Measures should be taken to reduce arsenic in drinking water in order to prevent hypothyroidism in rural areas.

Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas.

Exposure to arsenic causes many diseases. Most Americans in rural areas use groundwater for drinking, which may contain arsenic above the currently allowable level, 10µg/L. It is cost-effective to estimate groundwater arsenic levels based on data from wells with known arsenic concentrations. We compared the accuracy of several commonly used interpolation methods in estimating arsenic concentrations in >8000 wells in Texas by the leave-one-out-cross-validation technique. Correlation coefficient between measured and estimated arsenic levels was greater with inverse distance weighted (IDW) than kriging Gaussian, kriging spherical or cokriging interpolations when analyzing data from wells in the entire Texas (p<0.0001). Correlation coefficient was significantly lower with cokriging than any other methods (p<0.006) for wells in Texas, east Texas or the Edwards aquifer. Correlation coefficient was significantly greater for wells in southwestern Texas Panhandle than in east Texas, and was higher for wells in Ogallala aquifer than in Edwards aquifer (p<0.0001) regardless of interpolation methods. In regression analysis, the best models are when well depth and/or elevation were entered into the model as covariates regardless of area/aquifer or interpolation methods, and models with IDW are better than kriging in any area/aquifer. In conclusion, the accuracy in estimating groundwater arsenic level depends on both interpolation methods and wells' geographic distributions and characteristics in Texas. Taking well depth and elevation into regression analysis as covariates significantly increases the accuracy in estimating groundwater arsenic level in Texas with IDW in particular.

A proteomic signature for dementia with Lewy bodies.

INTRODUCTION: We sought to determine if a proteomic profile approach developed to detect Alzheimer's disease would distinguish patients with Lewy body disease from normal controls, and if it would distinguish dementia with Lewy bodies (DLB) from Parkinson's disease (PD). METHODS: Stored plasma samples were obtained from 145 patients (DLB n = 57, PD without dementia n = 32, normal controls n = 56) enrolled from patients seen in the Behavioral Neurology or Movement Disorders clinics at the Mayo Clinic, Florida. Proteomic assays were conducted and analyzed as per our previously published protocols. RESULTS: In the first step, the proteomic profile distinguished the DLB-PD group from controls with a diagnostic accuracy of 0.97, sensitivity of 0.91, and specificity of 0.86. In the second step, the proteomic profile distinguished the DLB from PD groups with a diagnostic accuracy of 0.92, sensitivity of 0.94, and specificity of 0.88. DISCUSSION: These data provide evidence of the potential utility of a multitiered blood-based proteomic screening method for detecting DLB and distinguishing DLB from PD.